Currently, in the radio communication field, the multi-antenna diversity technology is developing rapidly. For example, space-time coding technologies such as space-time block coding (Space-Time Block Coding, STBC) and space-time transmit diversity (Space-Time Transmit Diversity, STTD) are multi-antenna diversity technologies specific to point-to-point links. However, such technologies are primarily intended to increase diversity gain of the point-to-point links, and it is very difficult to accomplish multi-user diversity gain in a multi-user environment.
The multi-user diversity gain enables not only selecting when to transmit data, but also selecting one or more users who will transmit the data, and selecting power allocation between users in the multi-user environment. The performance gain unavailable from a point-to-point environment but available from such additional selections is multi-user diversity gain.
An Alamouti scheme is a typical multi-antenna diversity technology currently applied in the multi-user environment. The scheme includes: multiple users in a system measure a transient channel quality information (Channel Quality Information, CQI) value in a scheduling subband through pilot signals, and then feed back the transient CQI value through an uplink channel to an Evolved Universal Terrestrial Radio Access Network NodeB (E-UTRAN NodeB, eNB), and finally, the eNB uses a scheduling algorithm to schedule the users. Examples of common scheduling algorithms include maximum channel quality information scheduling (Max-CQI scheduling) and proportional fair scheduling (Proportional Fair scheduling, PF scheduling).
For example, with the Max-CQI algorithm, the eNB invokes the user having the highest CQI value to transmit data. In this way, when the user channel fluctuates slowly, the eNB always schedules one or more users with the best channel quality. Therefore, the scheduling fairness is not ensured in the multi-user environment.
As another example, with the PF algorithm, the eNB performs scheduling according to the value of a parameter k of the user. With a greater value of k, the user is more likely to be scheduled. The value of k is a result of dividing the CQI value of the user at the current time by the amount of data transmitted when the user is previously scheduled. Evidently, if the user has been scheduled for many times, the value of k diminishes, and the user will not be scheduled again. This algorithm ensures the scheduling fairness, but leads to a huge loss of system capacity because the scheduled user is not necessarily the user who has the best channel quality currently. Scheduling the user with the best current channel quality is a linchpin of ensuring the system capacity.
Therefore, in the multi-user environment in the prior art, if the user channel changes slowly, the system capacity is ensured maximally at the cost of the system fairness, and the system fairness is ensured at the cost of the system capacity. That is, in the prior art, it is very hard to keep a balance between the maximized system capacity and the user scheduling fairness.